Title :
Back Propagation Neural Network Applied to Modeling of Switched Reluctance Motor
Author :
Sun, Jianbo ; Zhan, Qionghua ; Guo, Youguang ; Zhu, Jianguo
Author_Institution :
Dept. of Electr. Machinery, Huazhong Univ. of Sci. & Technol., Hubei
Abstract :
This paper presents a back propagation neural network (BPNN) application for modeling of SRM, incorporating finite element analysis. Firstly, the magnetic curve of ferromagnetic material is smoothed by a BPNN. Secondly, this paper deduces the formula of magnetic force based on the local Jacobian derivative method and the magnetic vector potential. Thirdly, the determination of the optimal BPNN structures and learning times is introduced. At last, a dynamic model of SRM based on BPNNs is constructed. The validity of the model is proved by comparing the simulation results with the experimental results
Keywords :
backpropagation; electric machine analysis computing; ferromagnetic materials; finite element analysis; magnetic forces; neural nets; reluctance motors; back propagation neural network; ferromagnetic material; finite element analysis; local Jacobian derivative method; magnetic curve; magnetic force; magnetic vector potential; switched reluctance motor; Couplings; Jacobian matrices; Magnetic analysis; Magnetic flux; Magnetic forces; Magnetic materials; Neural networks; Reluctance machines; Reluctance motors; Torque;
Conference_Titel :
Electromagnetic Field Computation, 2006 12th Biennial IEEE Conference on
Conference_Location :
Miami, FL
Print_ISBN :
1-4244-0320-0
DOI :
10.1109/CEFC-06.2006.1632943
